• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.5
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• pp.1-9
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• 2010

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration ($ET_0$) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, Korea. Four climate factors, daily maximum temperature ($T_{max}$), daily minimum temperature ($T_{min}$), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 (Hadley Centre Coupled Model, ver.3) A1B scenario. The evapotranspirations were 549.7 mm/yr in baseline period (1973-2008), 558.1 mm/yr in 2011-2030, 593.0 mm/yr in 2046-2065, and 641.1 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.65-71
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• 2012

The objective of this study was to estimate surface runoff from rice paddy plots using an artificial neural network (ANN). A field experiment with three treatment levels was conducted in the NICS saemangum experimental field located in Iksan, Korea. The ANN model with the optimal network architectures, named Paddy1901 with 19 input nodes, 1 hidden layer with 16 neurons nodes, and 1 output node, was adopted to predict surface runoff from the plots. The model consisted of 7 parameters of precipitation, irrigation rate, ponding depth, average temperature, relative humidity, wind speed, and solar radiation on the daily basis. Daily runoff, as the target simulation value, was computed using a water balance equation. The field data collected in 2011 were used for training and validation of the model. The model was trained based on the error back propagation algorithm with sigmoid activation function. Simulation results for the independent training and testing data series showed that the model can perform well in simulating surface runoff from the study plots. The developed model has a main advantage that there is no requirement for any prior assumptions regarding the processes involved. ANN model thus can be a good tool to predict surface runoff from rice paddy fields.

• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.41-49
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• 2021

The causes of climate change are natural and artificial. Natural causes include changes in temperature and sunspot activities caused by changes in solar radiation due to large-scale volcanic activities, while artificial causes include increased greenhouse gas concentrations and land use changes. Studies have shown that excessive carbon use among artificial causes has accelerated global warming. Climate change is rapidly under way because of this. Due to climate change, the frequency and cycle of infectious disease viruses are greater and faster than before. Currently, the world is suffering greatly from coronavirus infection-19 (COVID-19). Korea is no exception. The first confirmed case occurred on January 20, 2020, and the number of infected people has steadily increased due to several waves since then, and many confirmed cases are occurring in 2021. In this study, we conduct a study on climate change before and after COVID-19 using weather data from Korea to determine whether climate change affects infectious disease viruses through logistic regression analysis. Based on this, we want to classify before and after COVID-19 through a logistic regression model to see how much classification rate we have. In addition, we compare monthly classification rates to see if there are seasonal classification differences.

• Membrane and Water Treatment
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• v.13 no.5
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• pp.219-225
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• 2022

5th Assessment Report of the Intergovernmental Panel on Climate Change Weather (AR5) predicts that recent severe hydrological events will affect the quality of water and increase water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed, and solar radiation) were compiled into a representative concentration curve (RC), defined using 8.5. AR5 and future use are calculated based on land use. Semi-distributed emission model Calculate emissions for each target period. Meteorological factors affecting water quality (precipitation, temperature, and flow) were input into a multiple linear regression (MLR) model and an artificial neural network (ANN) to analyze the data. Extensive experimental studies of flow properties have been carried out. In addition, an Acoustic Doppler Velocity (ADV) device was used to monitor the flow of a large open channel connection in a wastewater treatment plant in Ho Chi Minh City. Observations were made along different streams at different locations and at different depths. Analysis of measurement data shows average speed profile, aspect ratio, vertical position Measure, and ratio the vertical to bottom distance for maximum speed and water depth. This result indicates that the transport effect of the compound was considered when preparing the hazard analysis.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.31-44
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• 2024

People have been using more energy in the last years. Several research studies were conducted to develop sustainable energy sources that can produce clean energy to fulfill our energy requirements. Using renewable energy sources helps to decrease the harm to the environment caused by conventional power plants. Choosing the right location and capacity for DG-RESs can greatly impact the performance of Radial Distribution Systems. It is beneficial to have a good and stable electrical power supply with low energy waste and high effectiveness because it improves the performance and reliability of the system. This research investigates the ideal location and size for solar and wind power systems, which are popular methods for producing clean electricity. A new artificial intelligent algorithm called Nutcracker Optimization Algorithm (NOA) is used to find the best solution in two common electrical systems named IEEE 33 and 69 bus systems to examine the improvement in the efficiency & reliability of power system network by reducing power losses, making voltage deviation smaller, and improving voltage stability. Finally, the NOA method is compared with another method called PSO and developed Hybrid Algorithm (NOA+PSO) to validate the proposed algorithm effectiveness and enhancement of both efficiency and reliability aspects.

• Journal of the Korean earth science society
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• v.43 no.1
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• pp.224-235
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• 2022

This study aimed to confirm the status of media reports on space hazards using topic modeling analysis of media articles that are related to space hazards for the past 12 years. Therefore, Latent Dirichlet Allocation (LDA) analysis was performed by collecting over 1200 space hazards articles between 2010 and 2021 on solar storm, artificial space objects, and natural space objects from BIGKins news platform. The articles related to solar storm focused on three topics: the effect of solar explosion on satellites; effect of solar explosion on radio communication in Korea, centered on the Korean Space Weather Center; and relationship between aircrew and space radiation. The articles related to artificial space objects focused on three topics: the threat of space garbage to satellite and space stations and the transition of useful objects into space junk; the relationship between space garbage and humanity as shown in movies; and the effort of developed countries for tracking, monitoring, and disposing of space garbage. The articles related to natural space objects focused on two topics: International Space Agency's tracking and monitoring of near-Earth asteroids and the countermeasures of collisions, and the evolution and extinction of dinosaurs and mammals, with a focus on the collisions of asteroids or comets. Therefore, this study confirmed that domestic media play a role in conveying dangers of space hazards and arousing the attention of public using a total of eight themes in various fields such as society and culture, and derived education method and policy on space hazards.

• Food Science and Preservation
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• v.12 no.2
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• pp.184-189
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• 2005

In order to select probiotics having both a high survival rate and an ability to inhibit virulent pathogens, we have screened lactic acid bacteria and yeasts from Jeot-gal to examine their resistance to artificial gastric and bile juice. After being introduced in the artificial gastric acid for 2 hr, the isolated lactic acid bacteria and yeast were incubated for 24 hrs in the artificial bile juice. In particular, the strain ML 36, ML 128, and ML 178 survived the longest during 2 hr incubation period in the artificial gastric acid. All 3 strains of lactic acid bacteria, and 2 strains of yeast demonstrated higher growth rates than control in the artificial bile. In addition, the antimicrobial activity of lactic acid bacteria and yeasts was investigated to determine their efficiency as probiotic organisms. The lactic acid bacteria inhibited Gram positive and negative bacteria, while the yeast was marginally inhibited.

• Journal of Environmental Health Sciences
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• v.29 no.1
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• pp.51-61
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• 2003

Pollution purification using titanium dioxide (TiO$_2$) photocatalyst has attracted a great deal of attention with increasing number of relent environmental problems. Currently, the application of TiO$_2$ photocatalyst has been focused on purification and treatment of waste water. However. the use of conventional TiO$_2$ powder photocatalyst results in disadvantage of stirring during the reaction and of separation after the reaction. And the usage of artificial UV lamp has made the cost of photocatalyst treatment system high. Consequently, we herein studied the pilot-scale design to aid in optimization of the energy-saving process for more through development and reactor design by solar light/UV lamp/ TiO$_2$system. In this study, we manufactured the TiO$_2$sol by sol-gel method. According to analysis by XRD, SEM and TEM, characterization of TiO$_2$ sol were nano-size (5-6 nm) and anatase type. Inorganic binder (SiO$_2$) was added to TiO$_2$ lot to be coated for support strongly, and support of ceramic bead was used to lower separation rate that of glass bead The influences were studied of various experimental parameters such as TiO$_2$ quantity, pH, flow rate. additives, pollutants concentration, climate condition and reflection plate by means of reaction time of the main chararteristics of the obtained materials. In water treatment system, variable realtor as solar light/ or UV lamp according to climate condition such as sunny and cloudy days treated the phenol and E-coli(Escherichia coli) effectively.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.643-650
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• 2017

Photovoltaic generation which has unlimited energy sources are very intermittent because they depend on the weather. Therefore, it is necessary to get accurate generation prediction with reducing the uncertainty of photovoltaic generation and improvement of the economics. The Meteorological Agency predicts weather factors for three days, but doesn't predict the sunshine and solar radiation that are most correlated with the prediction of photovoltaic generation. In this study, we predict sunshine and solar radiation using weather, precipitation, wind direction, wind speed, humidity, and cloudiness which is forecasted for three days at Meteorological Agency. The photovoltaic generation forecasting model is proposed by using predicted solar radiation and sunshine. As a result, the proposed model showed better results in the error rate indexes such as MAE, RMSE, and MAPE than the model that predicts photovoltaic generation without radiation and sunshine. In addition, DNN showed a lower error rate index than using SVM, which is a type of machine learning.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.39-55
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• 2019

In this study, we have proposed an improved method to detect forest fires by correcting the reflected signals of day images using the middle-wavelength infrared (MWIR) channel. The proposed method is allowed to remove the reflected signals only using the image itself without an existing data source such as a land-cover map or atmospheric data. It includes the processing steps for calculating a solar-reflected signal such as 1) a simple correction model of the atmospheric transmittance for the MWIR channel and 2) calculating the image-based reflectance. We tested the performance of the method using the MODIS product. When compared to the conventional MODIS fire detection algorithm (MOD14 collection 6), the total number of detected fires was improved by approximately 17%. Most of all, the detection of fires improved by approximately 30% in the high reflection areas of the images. Moreover, the false alarm caused by artificial objects was clearly reduced and a confidence level analysis of the undetected fires showed that the proposed method had much better performance. The proposed method would be applicable to most satellite sensors with MWIR and thermal infrared channels. Especially for geostationary satellites such as GOES-R, HIMAWARI-8/9 and GeoKompsat-2A, the short acquisition time would greatly improve the performance of the proposed fire detection algorithm because reflected signals in the geostationary satellite images frequently vary according to solar zenith angle.